Stats

Installing the Intel® Distribution for Python and Intel® Performance Libraries with pip and PyPI

Installing the Intel® Distribution for Python and Intel® Performance Libraries with pip and PyPI

Editorial Note

This article is in the Product Showcase section for our sponsors at CodeProject. These articles are intended to provide you with information on products and services that we consider useful and of value to developers.

Performance Packages

The two most popular packages in numerical and scientific work (numpy and scipy) are available with the following commands below. For more information on the nature of their accelerations and performance benchmarks, please visit the link here.

Also, Intel-optimized-scikit-learn, pydaal(Intel® DAAL in Python) and tbb4py(Intel® TBB for Python) are also available on PyPI now.

Based on PyPI's dependency resolution on Intel variants, If one installs intel-numpy, one would also get mkl_fft and mkl_random (with NumPy). Similarly, if one installs intel-scipy, one would also get intel-numpy along with SciPy. And, if one installs intel-scikit-learn, one would also get intel-numpy,intel-scipy along with Scikit-Learn.

Note: If standard NumPy, SciPy and Scikit-Learn packages are already installed, the packages must be uninstalled before installing the Intel® variants of these packages(intel-numpy etc) to avoid any conflicts. As mentioned earlier, pydaal uses intel-numpy, hence it is important to first remove the standard Numpy library(if installed) and then install pydaal.

To uninstall existing packages, run the command:

pip uninstall numpy scipy scikit-learn -y

Specialized NumPy packages

Several specialized Intel packages act as a complement to numpy and scipy, which provide accelerated Fast Fourier Transforms and improved Random functionality through the MKL when paired with numpy and scipy.

Package Name

pip command

Platform Availability

mkl_fft

pip install mkl_fft

Linux, Win, macOS(10.12)

mkl_random

pip install mkl_random

Note: In order to utilize these packages, the standard NumPy installation must be removed first using the command: pip uninstall numpy -y

Intel® Runtime Packages

The runtime packages are built runtime distributable libraries that allow for dispatch of vectorization on Intel hardware. For Python packages that depend on these runtimes, they can be individually downloaded as well. For more information, please visit the link here.

Package Name

pip command

Platform Availability

mkl

pip install mkl

Linux, Win, macOS(10.12)

ipp

pip install ipp

daal

pip install daal

intel-openmp

pip install intel-openmp

tbb

pip install tbb

impi

pip install impi

Linux, Win

Development only packages

For those building their own Python packages with Intel® Parallel Studio XE or building and linking with the Intel® Performance Libraries, the devel packages assist in providing the development runtimes pre-built for testing, and are available with the following commands:

Package Name

pip command

Platform Availability

mkl-devel

pip install mkl-devel

Linux, Win, macOS(10.12)

ipp-devel

pip install ipp-devel

daal-devel

pip install daal-devel

Troubleshooting

While `pip install`-ing any package, if installation fails with the following error message :

Share

About the Author

You may know us for our processors. But we do so much more. Intel invents at the boundaries of technology to make amazing experiences possible for business and society, and for every person on Earth.

Harnessing the capability of the cloud, the ubiquity of the Internet of Things, the latest advances in memory and programmable solutions, and the promise of always-on 5G connectivity, Intel is disrupting industries and solving global challenges. Leading on policy, diversity, inclusion, education and sustainability, we create value for our stockholders, customers and society.